{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/method/position-wise-feed-forward-layer/papers/45","list_of":"/method/position-wise-feed-forward-layer","method":"Position-Wise Feed-Forward Layer","archive":{"snapshot":"2025-07-28"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"date (newest first), then slug","page":45,"pages_in_order":139,"rows_per_page":100,"rows":[4401,4500],"of":13895,"counts":{"archive_papers_tagged":13895,"with_a_code_link":6514,"where_syntology_ran_a_sample":2229,"not_listed_spam_title":0,"listed":13895,"listed_where_code_ran":2229,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":1902,"every_run_a_failure_of_syntologys_instrument":327,"listed_with_a_run_with_no_instrument_failure":1902,"listed_every_run_a_failure_of_syntologys_instrument":327,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/method/position-wise-feed-forward-layer","prev":"/method/position-wise-feed-forward-layer/papers/44","next":"/method/position-wise-feed-forward-layer/papers/46","papers":[{"paper":null,"slug":"multilateral-temporal-view-pyramid","title":"Mumpy: Multilateral Temporal-view Pyramid Transformer for Video Inpainting Detection","date":"2024-04-17","arxiv_id":"2404.11054","n_code_links":0,"syntology":null},{"paper":null,"slug":"octopus-v3-technical-report-for-on-device-sub","title":"Octopus v3: Technical Report for On-device Sub-billion Multimodal AI Agent","date":"2024-04-17","arxiv_id":"2404.11459","n_code_links":0,"syntology":null},{"paper":null,"slug":"pretraining-billion-scale-geospatial","title":"Pretraining Billion-scale Geospatial Foundational Models on Frontier","date":"2024-04-17","arxiv_id":"2404.11706","n_code_links":0,"syntology":null},{"paper":null,"slug":"prompt-optimizer-of-text-to-image-diffusion","title":"Prompt Optimizer of Text-to-Image Diffusion Models for Abstract Concept Understanding","date":"2024-04-17","arxiv_id":"2404.11589","n_code_links":0,"syntology":null},{"paper":"/paper/rd2bench-toward-data-centric-automatic-r-d","slug":"rd2bench-toward-data-centric-automatic-r-d","title":"Towards Data-Centric Automatic R&D","date":"2024-04-17","arxiv_id":"2404.11276","n_code_links":1,"syntology":null},{"paper":null,"slug":"revisiting-noise-resilience-strategies-in","title":"Revisiting Noise Resilience Strategies in Gesture Recognition: Short-Term Enhancement in Surface Electromyographic Signal Analysis","date":"2024-04-17","arxiv_id":"2404.11213","n_code_links":0,"syntology":null},{"paper":null,"slug":"self-adaptive-psro-towards-an-automatic","title":"Self-adaptive PSRO: Towards an Automatic Population-based Game Solver","date":"2024-04-17","arxiv_id":"2404.11144","n_code_links":0,"syntology":null},{"paper":null,"slug":"supervised-contrastive-vision-transformer-for","title":"Supervised Contrastive Vision Transformer for Breast Histopathological Image Classification","date":"2024-04-17","arxiv_id":"2404.11052","n_code_links":0,"syntology":null},{"paper":"/paper/towards-coarse-to-fine-evaluation-of","slug":"towards-coarse-to-fine-evaluation-of","title":"Towards Coarse-to-Fine Evaluation of Inference Efficiency for Large Language Models","date":"2024-04-17","arxiv_id":"2404.11502","n_code_links":1,"syntology":null},{"paper":"/paper/training-transformer-models-by-wavelet-losses","slug":"training-transformer-models-by-wavelet-losses","title":"Training Transformer Models by Wavelet Losses Improves Quantitative and Visual Performance in Single Image Super-Resolution","date":"2024-04-17","arxiv_id":"2404.11273","n_code_links":1,"syntology":{"ran":14,"of":17,"n_ran_checked":7,"n_instrument":7,"unverified":3,"pointer_only":7,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 7 where Syntology's instrument failed) · 3 unverified","official":{"repos":["mandalinadagi/wavelettention"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"aghint-attribute-guided-representation","title":"AGHINT: Attribute-Guided Representation Learning on Heterogeneous Information Networks with Transformer","date":"2024-04-16","arxiv_id":"2404.10443","n_code_links":0,"syntology":null},{"paper":null,"slug":"anomaly-correction-of-business-processes","title":"Anomaly Correction of Business Processes Using Transformer Autoencoder","date":"2024-04-16","arxiv_id":"2404.10211","n_code_links":0,"syntology":null},{"paper":"/paper/can-language-models-solve-olympiad","slug":"can-language-models-solve-olympiad","title":"Can Language Models Solve Olympiad Programming?","date":"2024-04-16","arxiv_id":"2404.10952","n_code_links":1,"syntology":{"ran":0,"of":1,"n_ran_checked":0,"n_instrument":0,"unverified":1,"pointer_only":1,"phrase":"0 ran · 1 unverified","official":{"repos":["princeton-nlp/USACO"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"paper":null,"slug":"cotar-chain-of-thought-attribution-reasoning","title":"CoTAR: Chain-of-Thought Attribution Reasoning with Multi-level Granularity","date":"2024-04-16","arxiv_id":"2404.10513","n_code_links":0,"syntology":null},{"paper":"/paper/deep-learning-and-llm-based-methods-applied","slug":"deep-learning-and-llm-based-methods-applied","title":"Deep Learning and LLM-based Methods Applied to Stellar Lightcurve Classification","date":"2024-04-16","arxiv_id":"2404.10757","n_code_links":1,"syntology":null},{"paper":"/paper/gasformer-a-transformer-based-architecture","slug":"gasformer-a-transformer-based-architecture","title":"Gasformer: A Transformer-based Architecture for Segmenting Methane Emissions from Livestock in Optical Gas Imaging","date":"2024-04-16","arxiv_id":"2404.10841","n_code_links":1,"syntology":null},{"paper":"/paper/how-faithful-are-rag-models-quantifying-the","slug":"how-faithful-are-rag-models-quantifying-the","title":"ClashEval: Quantifying the tug-of-war between an LLM's internal prior and external evidence","date":"2024-04-16","arxiv_id":"2404.10198","n_code_links":1,"syntology":null},{"paper":"/paper/incubating-text-classifiers-following-user","slug":"incubating-text-classifiers-following-user","title":"Incubating Text Classifiers Following User Instruction with Nothing but LLM","date":"2024-04-16","arxiv_id":"2404.10877","n_code_links":1,"syntology":null},{"paper":null,"slug":"mathwriting-a-dataset-for-handwritten","title":"MathWriting: A Dataset For Handwritten Mathematical Expression Recognition","date":"2024-04-16","arxiv_id":"2404.10690","n_code_links":0,"syntology":null},{"paper":"/paper/minicheck-efficient-fact-checking-of-llms-on","slug":"minicheck-efficient-fact-checking-of-llms-on","title":"MiniCheck: Efficient Fact-Checking of LLMs on Grounding Documents","date":"2024-04-16","arxiv_id":"2404.10774","n_code_links":2,"syntology":{"ran":2,"of":8,"n_ran_checked":2,"n_instrument":0,"unverified":6,"pointer_only":0,"phrase":"2 ran (of which 1 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 6 unverified","official":{"repos":["liyan06/minicheck"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":2,"n_unverified":6,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"neuromorphic-vision-based-motion-segmentation","title":"Neuromorphic Vision-based Motion Segmentation with Graph Transformer Neural Network","date":"2024-04-16","arxiv_id":"2404.10940","n_code_links":0,"syntology":null},{"paper":"/paper/search-beyond-queries-training-smaller","slug":"search-beyond-queries-training-smaller","title":"Grounded Language Agent for Product Search via Intelligent Web Interactions","date":"2024-04-16","arxiv_id":"2404.10887","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":3,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["MultifacetedNLP/Web-Agents-Unsupervised"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/self-supervised-visual-preference-alignment","slug":"self-supervised-visual-preference-alignment","title":"Self-Supervised Visual Preference Alignment","date":"2024-04-16","arxiv_id":"2404.10501","n_code_links":1,"syntology":{"ran":10,"of":11,"n_ran_checked":6,"n_instrument":4,"unverified":1,"pointer_only":11,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 1 honoured, 1 violated, 4 with no contract checked; 4 where Syntology's instrument failed) · 1 unverified","official":{"repos":["Kevinz-code/SeVa"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"social-choice-for-ai-alignment-dealing-with","title":"Social Choice Should Guide AI Alignment in Dealing with Diverse Human Feedback","date":"2024-04-16","arxiv_id":"2404.10271","n_code_links":0,"syntology":null},{"paper":null,"slug":"tc-ocr-tablecraft-ocr-for-efficient-detection","title":"TC-OCR: TableCraft OCR for Efficient Detection & Recognition of Table Structure & Content","date":"2024-04-16","arxiv_id":"2404.10305","n_code_links":0,"syntology":null},{"paper":"/paper/threat-behavior-textual-search-by-attention","slug":"threat-behavior-textual-search-by-attention","title":"Threat Behavior Textual Search by Attention Graph Isomorphism","date":"2024-04-16","arxiv_id":"2404.10944","n_code_links":1,"syntology":null},{"paper":null,"slug":"aigen-an-adversarial-approach-for-instruction","title":"AIGeN: An Adversarial Approach for Instruction Generation in VLN","date":"2024-04-15","arxiv_id":"2404.10054","n_code_links":0,"syntology":null},{"paper":null,"slug":"eyeformer-predicting-personalized-scanpaths","title":"EyeFormer: Predicting Personalized Scanpaths with Transformer-Guided Reinforcement Learning","date":"2024-04-15","arxiv_id":"2404.10163","n_code_links":0,"syntology":null},{"paper":null,"slug":"learn-your-reference-model-for-real-good","title":"Learn Your Reference Model for Real Good Alignment","date":"2024-04-15","arxiv_id":"2404.09656","n_code_links":0,"syntology":null},{"paper":null,"slug":"llm-evaluators-recognize-and-favor-their-own","title":"LLM Evaluators Recognize and Favor Their Own Generations","date":"2024-04-15","arxiv_id":"2404.13076","n_code_links":0,"syntology":null},{"paper":null,"slug":"lorap-transformer-sub-layers-deserve","title":"LoRAP: Transformer Sub-Layers Deserve Differentiated Structured Compression for Large Language Models","date":"2024-04-15","arxiv_id":"2404.09695","n_code_links":0,"syntology":null},{"paper":null,"slug":"numerical-attributes-learning-for-cardiac","title":"Are Medium-Sized Transformers Models still Relevant for Medical Records Processing?","date":"2024-04-15","arxiv_id":"2404.10171","n_code_links":0,"syntology":null},{"paper":null,"slug":"odformer-semantic-fundus-image-segmentation","title":"ODFormer: Semantic Fundus Image Segmentation Using Transformer for Optic Nerve Head Detection","date":"2024-04-15","arxiv_id":"2405.09552","n_code_links":0,"syntology":null},{"paper":"/paper/segformer3d-an-efficient-transformer-for-3d","slug":"segformer3d-an-efficient-transformer-for-3d","title":"SegFormer3D: an Efficient Transformer for 3D Medical Image Segmentation","date":"2024-04-15","arxiv_id":"2404.10156","n_code_links":2,"syntology":{"ran":2,"of":2,"n_ran_checked":0,"n_instrument":2,"unverified":0,"pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","official":{"repos":["osupcvlab/segformer3d"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/state-space-model-for-new-generation-network","slug":"state-space-model-for-new-generation-network","title":"State Space Model for New-Generation Network Alternative to Transformers: A Survey","date":"2024-04-15","arxiv_id":"2404.09516","n_code_links":1,"syntology":null},{"paper":null,"slug":"unveiling-imitation-learning-exploring-the","title":"Unveiling Imitation Learning: Exploring the Impact of Data Falsity to Large Language Model","date":"2024-04-15","arxiv_id":"2404.09717","n_code_links":0,"syntology":null},{"paper":"/paper/witunet-a-u-shaped-architecture-integrating","slug":"witunet-a-u-shaped-architecture-integrating","title":"WiTUnet: A U-Shaped Architecture Integrating CNN and Transformer for Improved Feature Alignment and Local Information Fusion","date":"2024-04-15","arxiv_id":"2404.09533","n_code_links":1,"syntology":null},{"paper":"/paper/zero-shot-building-age-classification-from","slug":"zero-shot-building-age-classification-from","title":"Zero-shot Building Age Classification from Facade Image Using GPT-4","date":"2024-04-15","arxiv_id":"2404.09921","n_code_links":1,"syntology":null},{"paper":null,"slug":"arena-a-patch-of-interest-vit-inference","title":"Arena: A Patch-of-Interest ViT Inference Acceleration System for Edge-Assisted Video Analytics","date":"2024-04-14","arxiv_id":"2404.09245","n_code_links":0,"syntology":null},{"paper":"/paper/max-ast-combining-convolution-local-and","slug":"max-ast-combining-convolution-local-and","title":"MAX-AST: COMBINING CONVOLUTION, LOCAL AND GLOBAL SELF-ATTENTIONS FOR AUDIO EVENT CLASSIFICATION","date":"2024-04-14","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/rf-diffusion-radio-signal-generation-via-time","slug":"rf-diffusion-radio-signal-generation-via-time","title":"RF-Diffusion: Radio Signal Generation via Time-Frequency Diffusion","date":"2024-04-14","arxiv_id":"2404.09140","n_code_links":1,"syntology":{"ran":0,"of":3,"n_ran_checked":0,"n_instrument":0,"unverified":3,"pointer_only":3,"phrase":"0 ran · 3 unverified","official":{"repos":["mobicom24/rf-diffusion"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":[]}}},{"paper":null,"slug":"transformerfam-feedback-attention-is-working","title":"TransformerFAM: Feedback attention is working memory","date":"2024-04-14","arxiv_id":"2404.09173","n_code_links":0,"syntology":null},{"paper":null,"slug":"heat-head-level-parameter-efficient","title":"Rethinking Low-Rank Adaptation in Vision: Exploring Head-Level Responsiveness across Diverse Tasks","date":"2024-04-13","arxiv_id":"2404.08894","n_code_links":0,"syntology":null},{"paper":"/paper/neurit-pushing-the-limit-of-neural-inertial","slug":"neurit-pushing-the-limit-of-neural-inertial","title":"NeurIT: Pushing the Limit of Neural Inertial Tracking for Indoor Robotic IoT","date":"2024-04-13","arxiv_id":"2404.08939","n_code_links":1,"syntology":null},{"paper":"/paper/oovs-in-the-spotlight-how-to-inflect-them","slug":"oovs-in-the-spotlight-how-to-inflect-them","title":"OOVs in the Spotlight: How to Inflect them?","date":"2024-04-13","arxiv_id":"2404.08974","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-novel-vision-transformer-based-load-profile","title":"A Novel Vision Transformer based Load Profile Analysis using Load Images as Inputs","date":"2024-04-12","arxiv_id":"2404.08175","n_code_links":0,"syntology":null},{"paper":null,"slug":"calibration-reconstruction-deep-integrated","title":"Calibration & Reconstruction: Deep Integrated Language for Referring Image Segmentation","date":"2024-04-12","arxiv_id":"2404.08281","n_code_links":0,"syntology":null},{"paper":"/paper/constrained-c-test-generation-via-mixed","slug":"constrained-c-test-generation-via-mixed","title":"Constrained C-Test Generation via Mixed-Integer Programming","date":"2024-04-12","arxiv_id":"2404.08821","n_code_links":1,"syntology":null},{"paper":"/paper/dataset-reset-policy-optimization-for-rlhf","slug":"dataset-reset-policy-optimization-for-rlhf","title":"Dataset Reset Policy Optimization for RLHF","date":"2024-04-12","arxiv_id":"2404.08495","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":0,"n_instrument":3,"unverified":0,"pointer_only":2,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","official":{"repos":["cornell-rl/drpo"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"paper":null,"slug":"don-t-forget-to-put-the-milk-back-dataset-for","title":"\"Don't forget to put the milk back!\" Dataset for Enabling Embodied Agents to Detect Anomalous Situations","date":"2024-04-12","arxiv_id":"2404.08827","n_code_links":0,"syntology":null},{"paper":null,"slug":"ifvit-interpretable-fixed-length","title":"IFViT: Interpretable Fixed-Length Representation for Fingerprint Matching via Vision Transformer","date":"2024-04-12","arxiv_id":"2404.08237","n_code_links":0,"syntology":null},{"paper":"/paper/megalodon-efficient-llm-pretraining-and","slug":"megalodon-efficient-llm-pretraining-and","title":"Megalodon: Efficient LLM Pretraining and Inference with Unlimited Context Length","date":"2024-04-12","arxiv_id":"2404.08801","n_code_links":1,"syntology":{"ran":2,"of":3,"n_ran_checked":1,"n_instrument":1,"unverified":1,"pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","official":{"repos":["xuezhemax/megalodon"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"msstnet-a-multi-scale-spatio-temporal-cnn","title":"MSSTNet: A Multi-Scale Spatio-Temporal CNN-Transformer Network for Dynamic Facial Expression Recognition","date":"2024-04-12","arxiv_id":"2404.08433","n_code_links":0,"syntology":null},{"paper":null,"slug":"scalability-in-building-component-data","title":"Scalability in Building Component Data Annotation: Enhancing Facade Material Classification with Synthetic Data","date":"2024-04-12","arxiv_id":"2404.08557","n_code_links":0,"syntology":null},{"paper":null,"slug":"single-image-driven-3d-viewpoint-training","title":"Single-image driven 3d viewpoint training data augmentation for effective wine label recognition","date":"2024-04-12","arxiv_id":"2404.08820","n_code_links":0,"syntology":null},{"paper":"/paper/small-models-are-still-effective-cross-domain","slug":"small-models-are-still-effective-cross-domain","title":"Small Models Are (Still) Effective Cross-Domain Argument Extractors","date":"2024-04-12","arxiv_id":"2404.08579","n_code_links":1,"syntology":null},{"paper":"/paper/automatic-generation-and-evaluation-of","slug":"automatic-generation-and-evaluation-of","title":"Automatic Generation and Evaluation of Reading Comprehension Test Items with Large Language Models","date":"2024-04-11","arxiv_id":"2404.07720","n_code_links":2,"syntology":null},{"paper":"/paper/comments-as-natural-logic-pivots-improve-code","slug":"comments-as-natural-logic-pivots-improve-code","title":"Comments as Natural Logic Pivots: Improve Code Generation via Comment Perspective","date":"2024-04-11","arxiv_id":"2404.07549","n_code_links":1,"syntology":null},{"paper":"/paper/designqa-a-multimodal-benchmark-for","slug":"designqa-a-multimodal-benchmark-for","title":"DesignQA: A Multimodal Benchmark for Evaluating Large Language Models' Understanding of Engineering Documentation","date":"2024-04-11","arxiv_id":"2404.07917","n_code_links":1,"syntology":{"ran":5,"of":10,"n_ran_checked":5,"n_instrument":0,"unverified":5,"pointer_only":10,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","official":{"repos":["anniedoris/design_qa"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":5,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"event-enhanced-snapshot-compressive","title":"Event-Enhanced Snapshot Compressive Videography at 10K FPS","date":"2024-04-11","arxiv_id":"2404.07551","n_code_links":0,"syntology":null},{"paper":"/paper/from-words-to-numbers-your-large-language","slug":"from-words-to-numbers-your-large-language","title":"From Words to Numbers: Your Large Language Model Is Secretly A Capable Regressor When Given In-Context Examples","date":"2024-04-11","arxiv_id":"2404.07544","n_code_links":1,"syntology":{"ran":8,"of":10,"n_ran_checked":8,"n_instrument":0,"unverified":2,"pointer_only":10,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","official":{"repos":["robertvacareanu/llm4regression"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"graph-integrated-language-transformers-for","title":"Graph Integrated Language Transformers for Next Action Prediction in Complex Phone Calls","date":"2024-04-11","arxiv_id":"2404.08155","n_code_links":0,"syntology":null},{"paper":"/paper/hgrn2-gated-linear-rnns-with-state-expansion","slug":"hgrn2-gated-linear-rnns-with-state-expansion","title":"HGRN2: Gated Linear RNNs with State Expansion","date":"2024-04-11","arxiv_id":"2404.07904","n_code_links":4,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["sustcsonglin/flash-linear-attention","opennlplab/hgrn2"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"human-latency-conversational-turns-for-spoken","title":"Human Latency Conversational Turns for Spoken Avatar Systems","date":"2024-04-11","arxiv_id":"2404.16053","n_code_links":0,"syntology":null},{"paper":null,"slug":"latte-low-precision-approximate-attention","title":"LATTE: Low-Precision Approximate Attention with Head-wise Trainable Threshold for Efficient Transformer","date":"2024-04-11","arxiv_id":"2404.07519","n_code_links":0,"syntology":null},{"paper":null,"slug":"llm-agents-can-autonomously-exploit-one-day","title":"LLM Agents can Autonomously Exploit One-day Vulnerabilities","date":"2024-04-11","arxiv_id":"2404.08144","n_code_links":0,"syntology":null},{"paper":"/paper/lucf-net-lightweight-u-shaped-cascade-fusion","slug":"lucf-net-lightweight-u-shaped-cascade-fusion","title":"LUCF-Net: Lightweight U-shaped Cascade Fusion Network for Medical Image Segmentation","date":"2024-04-11","arxiv_id":"2404.07473","n_code_links":1,"syntology":null},{"paper":null,"slug":"mm-phyqa-multimodal-physics-question","title":"MM-PhyQA: Multimodal Physics Question-Answering With Multi-Image CoT Prompting","date":"2024-04-11","arxiv_id":"2404.08704","n_code_links":0,"syntology":null},{"paper":null,"slug":"post-hurricane-building-damage-assessment","title":"Post-hurricane building damage assessment using street-view imagery and structured data: A multi-modal deep learning approach","date":"2024-04-11","arxiv_id":"2404.07399","n_code_links":0,"syntology":null},{"paper":null,"slug":"remembering-transformer-for-continual","title":"Remembering Transformer for Continual Learning","date":"2024-04-11","arxiv_id":"2404.07518","n_code_links":0,"syntology":null},{"paper":null,"slug":"structure-aware-fine-tuning-for-code-pre","title":"Structure-aware Fine-tuning for Code Pre-trained Models","date":"2024-04-11","arxiv_id":"2404.07471","n_code_links":0,"syntology":null},{"paper":null,"slug":"token-space-a-category-theory-framework-for","title":"Token Space: A Category Theory Framework for AI Computations","date":"2024-04-11","arxiv_id":"2404.11624","n_code_links":0,"syntology":null},{"paper":"/paper/vim-unet-vision-mamba-for-biomedical","slug":"vim-unet-vision-mamba-for-biomedical","title":"ViM-UNet: Vision Mamba for Biomedical Segmentation","date":"2024-04-11","arxiv_id":"2404.07705","n_code_links":1,"syntology":null},{"paper":"/paper/control-dag-constrained-decoding-for-non","slug":"control-dag-constrained-decoding-for-non","title":"Control-DAG: Constrained Decoding for Non-Autoregressive Directed Acyclic T5 using Weighted Finite State Automata","date":"2024-04-10","arxiv_id":"2404.06854","n_code_links":1,"syntology":null},{"paper":"/paper/dynamic-generation-of-personalities-with","slug":"dynamic-generation-of-personalities-with","title":"Dynamic Generation of Personalities with Large Language Models","date":"2024-04-10","arxiv_id":"2404.07084","n_code_links":1,"syntology":null},{"paper":"/paper/leave-no-context-behind-efficient-infinite","slug":"leave-no-context-behind-efficient-infinite","title":"Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attention","date":"2024-04-10","arxiv_id":"2404.07143","n_code_links":5,"syntology":{"ran":15,"of":16,"n_ran_checked":7,"n_instrument":8,"unverified":1,"pointer_only":6,"phrase":"15 ran (of which 4 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 2 violated, 5 with no contract checked; 8 where Syntology's instrument failed) · 1 unverified","official":null}},{"paper":"/paper/nfarec-a-negative-feedback-aware-recommender","slug":"nfarec-a-negative-feedback-aware-recommender","title":"NFARec: A Negative Feedback-Aware Recommender Model","date":"2024-04-10","arxiv_id":"2404.06900","n_code_links":1,"syntology":null},{"paper":null,"slug":"characterizing-multimodal-long-form","title":"Characterizing Multimodal Long-form Summarization: A Case Study on Financial Reports","date":"2024-04-09","arxiv_id":"2404.06162","n_code_links":0,"syntology":null},{"paper":null,"slug":"comparing-two-model-designs-for-clinical-note","title":"Comparing Two Model Designs for Clinical Note Generation; Is an LLM a Useful Evaluator of Consistency?","date":"2024-04-09","arxiv_id":"2404.06503","n_code_links":0,"syntology":null},{"paper":null,"slug":"generative-pre-trained-transformer-for-2","title":"Generative Pre-Trained Transformer for Symbolic Regression Base In-Context Reinforcement Learning","date":"2024-04-09","arxiv_id":"2404.06330","n_code_links":0,"syntology":null},{"paper":"/paper/internlm-xcomposer2-4khd-a-pioneering-large","slug":"internlm-xcomposer2-4khd-a-pioneering-large","title":"InternLM-XComposer2-4KHD: A Pioneering Large Vision-Language Model Handling Resolutions from 336 Pixels to 4K HD","date":"2024-04-09","arxiv_id":"2404.06512","n_code_links":2,"syntology":null},{"paper":"/paper/llm2vec-large-language-models-are-secretly","slug":"llm2vec-large-language-models-are-secretly","title":"LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders","date":"2024-04-09","arxiv_id":"2404.05961","n_code_links":1,"syntology":null},{"paper":null,"slug":"llms-reading-comprehension-is-affected-by","title":"LLMs' Reading Comprehension Is Affected by Parametric Knowledge and Struggles with Hypothetical Statements","date":"2024-04-09","arxiv_id":"2404.06283","n_code_links":0,"syntology":null},{"paper":null,"slug":"mansformer-efficient-transformer-of-mixed","title":"Efficient Concertormer for Image Deblurring and Beyond","date":"2024-04-09","arxiv_id":"2404.06135","n_code_links":0,"syntology":null},{"paper":"/paper/pgtnet-a-process-graph-transformer-network","slug":"pgtnet-a-process-graph-transformer-network","title":"PGTNet: A Process Graph Transformer Network for Remaining Time Prediction of Business Process Instances","date":"2024-04-09","arxiv_id":"2404.06267","n_code_links":1,"syntology":null},{"paper":null,"slug":"sandwich-attack-multi-language-mixture","title":"Sandwich attack: Multi-language Mixture Adaptive Attack on LLMs","date":"2024-04-09","arxiv_id":"2404.07242","n_code_links":0,"syntology":null},{"paper":"/paper/scrdit-generating-single-cell-rna-seq-data-by","slug":"scrdit-generating-single-cell-rna-seq-data-by","title":"scRDiT: Generating single-cell RNA-seq data by diffusion transformers and accelerating sampling","date":"2024-04-09","arxiv_id":"2404.06153","n_code_links":1,"syntology":null},{"paper":null,"slug":"vision2ui-a-real-world-dataset-with-layout","title":"WebCode2M: A Real-World Dataset for Code Generation from Webpage Designs","date":"2024-04-09","arxiv_id":"2404.06369","n_code_links":0,"syntology":null},{"paper":null,"slug":"decision-transformer-for-wireless","title":"Decision Transformers for Wireless Communications: A New Paradigm of Resource Management","date":"2024-04-08","arxiv_id":"2404.05199","n_code_links":0,"syntology":null},{"paper":"/paper/deep-optics-for-video-snapshot-compressive-1","slug":"deep-optics-for-video-snapshot-compressive-1","title":"Deep Optics for Video Snapshot Compressive Imaging","date":"2024-04-08","arxiv_id":"2404.05274","n_code_links":1,"syntology":{"ran":4,"of":5,"n_ran_checked":4,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["pwangcs/deepopticssci"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"enhancing-lip-reading-with-multi-scale-video","title":"Enhancing Lip Reading with Multi-Scale Video and Multi-Encoder","date":"2024-04-08","arxiv_id":"2404.05466","n_code_links":0,"syntology":null},{"paper":null,"slug":"evaluation-of-an-llm-in-identifying-logical","title":"Evaluation of an LLM in Identifying Logical Fallacies: A Call for Rigor When Adopting LLMs in HCI Research","date":"2024-04-08","arxiv_id":"2404.05213","n_code_links":0,"syntology":null},{"paper":"/paper/fighting-crime-with-transformers-empirical","slug":"fighting-crime-with-transformers-empirical","title":"Fighting crime with Transformers: Empirical analysis of address parsing methods in payment data","date":"2024-04-08","arxiv_id":"2404.05632","n_code_links":1,"syntology":null},{"paper":"/paper/hsvit-horizontally-scalable-vision","slug":"hsvit-horizontally-scalable-vision","title":"HSViT: Horizontally Scalable Vision Transformer","date":"2024-04-08","arxiv_id":"2404.05196","n_code_links":1,"syntology":null},{"paper":"/paper/llm-reasoners-new-evaluation-library-and","slug":"llm-reasoners-new-evaluation-library-and","title":"LLM Reasoners: New Evaluation, Library, and Analysis of Step-by-Step Reasoning with Large Language Models","date":"2024-04-08","arxiv_id":"2404.05221","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":0,"n_instrument":3,"unverified":0,"pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":"/paper/mlp-can-be-a-good-transformer-learner","slug":"mlp-can-be-a-good-transformer-learner","title":"MLP Can Be A Good Transformer Learner","date":"2024-04-08","arxiv_id":"2404.05657","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["sihaoevery/lambda_vit"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/multi-head-attention-based-deep-multiple","slug":"multi-head-attention-based-deep-multiple","title":"Multi-head Attention-based Deep Multiple Instance Learning","date":"2024-04-08","arxiv_id":"2404.05362","n_code_links":1,"syntology":null},{"paper":null,"slug":"relation-extraction-using-large-language","title":"Relation Extraction Using Large Language Models: A Case Study on Acupuncture Point Locations","date":"2024-04-08","arxiv_id":"2404.05415","n_code_links":0,"syntology":null},{"paper":"/paper/use-of-a-structured-knowledge-base-enhances","slug":"use-of-a-structured-knowledge-base-enhances","title":"Use of a Structured Knowledge Base Enhances Metadata Curation by Large Language Models","date":"2024-04-08","arxiv_id":"2404.05893","n_code_links":1,"syntology":null},{"paper":"/paper/xiwu-a-basis-flexible-and-learnable-llm-for","slug":"xiwu-a-basis-flexible-and-learnable-llm-for","title":"Xiwu: A Basis Flexible and Learnable LLM for High Energy Physics","date":"2024-04-08","arxiv_id":"2404.08001","n_code_links":1,"syntology":null}],"record_sha256":"330821658f78ed54d07279fe571b90289b298c12125b55142e413ef541598038","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}